I am a PhD student in the StatML CDT program, working with Professor Chris Holmes since late 2020. During my PhD, I have interned as a Research Scientist at Google Research, where I developed a concept-based interpretability framework, and at IBM Research, where I created an interpretable balancing method to identify local natural experiments. Prior to my PhD, I earned an MSc in Applied Mathematics from Ecole Centrale Paris and an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine.

My PhD research is centered on AI Safety, with a focus on explainability, causal inference, and robustness. I aim to develop analytical methods that are solution-driven and applicable to real-world problems. Recently, I have been exploring topics related to Large Language Model (LLM) Alignment and Interpretability, particularly in the area of Mechanistic Interpretability.